DA0-002 Data Analysis Practice Test 8 — 15 Questions

Question 1

A healthcare analytics team is analyzing patient readmission rates. They have a dataset with thousands of records including patient age, diagnosis, length of stay, number of prior admissions, and discharge date. The goal is to identify key factors influencing readmission and create a model to predict high-risk patients. The data is imbalanced: only 5% of patients are readmitted within 30 days. The team plans to use logistic regression. What is the most appropriate approach?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Apply oversampling techniques like SMOTE to the training set. With imbalanced data, logistic regression can be biased toward the majority class. Oversampling the minority class (e.g., SMOTE) helps the model learn patterns for readmission. Using accuracy as a metric would be misleading. Removing majority samples discards valuable data. Using data as-is often fails to predict the minority class.

Answer

Use the dataset as is because logistic regression handles imbalance

Answer

Remove most of the non-readmitted patients to balance the dataset

Answer

Use accuracy as the evaluation metric

Question 2

A marketing analyst wants to segment customers based on purchasing behavior and demographics. The dataset includes continuous variables (spending amount, frequency) and categorical variables (region, gender). The analyst decides to use k-means clustering. What should the analyst do to prepare the data?

Accepted Answer

Standardize continuous variables and one-hot encode categorical variables. K-means clustering relies on Euclidean distance, which is sensitive to the scale of features. Standardizing continuous variables (e.g., spending amount, frequency) ensures they contribute equally to distance calculations, while one-hot encoding categorical variables (e.g., region, gender) converts them into numerical form without implying ordinal relationships, allowing k-means to process mixed data types correctly.

Answer

Use raw data because k-means works with mixed types

Answer

Apply PCA first to reduce dimensionality

Answer

Remove categorical variables entirely

Question 3

A data analyst is building a supervised learning model to predict customer churn. The target variable is binary (churn = yes/no). Which TWO modeling techniques are appropriate for this task? (Select two.)

Accepted Answer

Logistic regression. Logistic regression (C) is correct because it is a supervised classification technique that models the probability of a binary outcome (churn yes/no) using a sigmoid function, making it a standard choice for binary targets. Decision trees (D) are also correct because they are supervised classifiers that recursively split features to predict categorical class labels, and they handle binary targets naturally while offering interpretability. K-means clustering (A) is wrong because it is an unsupervised algorithm that groups unlabeled data and cannot predict a labeled binary target. Linear regression (B) is wrong because it predicts continuous numeric values rather than class probabilities or discrete categories, so it is unsuitable for binary classification. The Apriori algorithm (E) is wrong because it is an unsupervised association-rule mining method for finding frequent itemsets, not a predictive classification model.

Answer

K-means clustering

Answer

Linear regression

Answer

Apriori algorithm

Question 4

A data analyst at a retail company is building a multiple linear regression model to forecast weekly sales. The dataset contains 50 predictor variables, including store size, promotional spend, holiday indicators, and many others. After training the model, the analyst observes an R-squared of 0.99 on the training set but only 0.55 on the holdout test set. Which action should the analyst take first to address this discrepancy?

Accepted Answer

Remove highly correlated predictor variables and apply regularization (e.g., Ridge or Lasso).. The high R-squared of 0.99 on training data versus 0.55 on test data is a classic sign of overfitting, where the model has learned noise and specific patterns in the training set that do not generalize. Removing highly correlated predictors reduces multicollinearity and model complexity, while regularization (Ridge or Lasso) penalizes large coefficients, shrinking them to prevent overfitting. This is the most direct first step to improve generalization.

Answer

Add more predictor variables to increase the training R-squared further.

Answer

Use k-fold cross-validation with a different random seed to get a more reliable test set estimate.

Answer

Increase the number of hidden layers in the model to capture more complexity.

Question 5

A marketing analyst wants to segment customers based on their purchase history, including total spent, number of transactions, and average order value. The analyst runs k-means clustering with k=5 on the raw data but notices that the cluster assignments change significantly every time the algorithm is executed. What should the analyst do first to obtain consistent and meaningful clusters?

Accepted Answer

Normalize the features and set a fixed random seed for the initial centroids.. The instability in cluster assignments is caused by the algorithm's sensitivity to the scale of features and the random initialization of centroids. Normalizing the features ensures that each variable contributes equally to the distance calculations, while setting a fixed random seed makes the initial centroid selection deterministic, leading to reproducible results.

Answer

Switch to hierarchical clustering, which does not require specifying k.

Answer

Increase the number of clusters to k=10 to capture more detail.

Answer

Use principal component analysis (PCA) to reduce the number of features to two.

Question 6

An analyst computed the mean, median, and mode of a dataset and found they are all equal. Which of the following best describes the distribution?

Accepted Answer

Symmetric. When mean, median, and mode are equal, the distribution is symmetric and unimodal, often resembling a normal distribution.

Answer

Bimodal

Answer

Negatively skewed

Answer

Positively skewed

Question 7

A data analyst is performing a multiple linear regression with three predictors. The model output shows an R-squared of 0.85 and an adjusted R-squared of 0.80. Which of the following is the best interpretation of the difference between these two values?

Accepted Answer

One or more predictors may not be contributing meaningfully. R-squared (0.85) measures how much variance the model explains, while adjusted R-squared (0.80) penalizes for the number of predictors and only increases if a new predictor improves the model more than chance would predict. A notable drop between the two (0.05) suggests that at least one predictor is not contributing meaningfully and may be adding noise rather than explanatory power.

Answer

The model is overfitted, so all predictors should be removed

Answer

The model has high multicollinearity

Answer

The residuals are not normally distributed

Question 8

A data scientist is preparing data for a K-means clustering algorithm. The dataset contains features measured in different units (e.g., income in dollars and age in years). Which preprocessing step is most critical before running K-means?

Accepted Answer

Standardize or normalize the features. K-means clustering uses Euclidean distance to measure similarity between data points. If features are on different scales (e.g., income in dollars vs. age in years), the feature with the larger range will dominate the distance calculation, leading to biased clusters. Standardizing (z-score normalization) or normalizing (min-max scaling) the features ensures that all features contribute equally to the distance metric, which is critical for K-means to produce meaningful clusters.

Answer

Remove outliers

Answer

Encode categorical variables

Answer

Perform feature selection

Question 9

In a time series analysis, a retail analyst observes consistent peaks in sales every December and troughs every February. This pattern repeats annually. Which component of time series does this represent?

Accepted Answer

Seasonality. Seasonality refers to regular patterns that repeat over fixed periods, such as months or quarters.

Answer

Irregular

Answer

Trend

Answer

Cyclical

Question 10

A data analyst runs an A/B test on a new website layout. The test yields a p-value of 0.04 with the null hypothesis being no difference in conversion rates. The significance threshold is α=0.05. Which of the following is the correct conclusion?

Accepted Answer

Reject the null hypothesis; there is a statistically significant difference in conversion rates.. Since p-value (0.04) < α (0.05), we reject the null hypothesis and conclude there is a statistically significant difference. However, statistical significance does not guarantee practical significance.

Answer

The result is not significant; accept the alternative hypothesis.

Answer

Reject the null hypothesis; the new layout is proven to increase conversions.

Answer

Fail to reject the null hypothesis; there is no evidence of a difference.

Question 11

A dataset contains a column 'Age' with values: [22, 25, 25, 30, 35, 40, 45]. What is the interquartile range (IQR)?

Accepted Answer

15. The interquartile range (IQR) is Q3 minus Q1. For the dataset [22, 25, 25, 30, 35, 40, 45], the median is 30. Q1 is the median of the lower half [22, 25, 25] = 25. Q3 is the median of the upper half [35, 40, 45] = 40. IQR = 40 - 25 = 15.

Answer

10

Answer

20

Answer

25

Question 12

A data analyst wants to understand the relationship between advertising spend and sales revenue. The analyst calculates a Pearson correlation coefficient of 0.85. Which of the following is the best interpretation?

Accepted Answer

There is a strong positive linear relationship between advertising spend and sales.. A Pearson correlation coefficient of 0.85 indicates a strong positive linear relationship between the two variables. The value is close to +1, meaning as advertising spend increases, sales revenue tends to increase in a linear fashion. Correlation measures the strength and direction of a linear association, not causation or predictive proportion.

Answer

85% of the variation in sales is explained by advertising spend.

Answer

Increasing advertising spend by $1 will increase sales by $0.85.

Answer

There is a strong negative linear relationship between advertising spend and sales.

Question 13

In a logistic regression model predicting customer churn (1 = churn, 0 = not churn), the coefficient for 'contract length' is -0.5. Which of the following is the correct interpretation?

Accepted Answer

For each unit increase in contract length, the log-odds of churn decrease by 0.5.. In logistic regression, the coefficient represents the change in log-odds of the outcome for a one-unit increase in the predictor, holding other variables constant. A coefficient of -0.5 means that for each unit increase in contract length, the log-odds of churn decrease by 0.5. This is the correct interpretation of the model's output.

Answer

Longer contract length increases the odds of churn.

Answer

The probability of churn decreases by 50% for each unit increase in contract length.

Answer

Contract length is not a significant predictor.

Question 14

A data analyst is cleaning a dataset and finds that 5% of values in the 'income' column are missing. The analyst decides to impute missing values using the mean of the non-missing values. Which potential issue should the analyst be most concerned about?

Accepted Answer

The imputation may reduce the variance and distort the distribution.. Mean imputation replaces missing values with the average of observed values, which pulls imputed values toward the center of the distribution. This artificially reduces the variance and can distort the shape of the distribution (e.g., making it more peaked), biasing downstream statistics and models. The analyst should be most concerned about this distortion rather than the missing rate itself.

Answer

The imputation is not valid because the missing rate is too low.

Answer

The imputation will increase the standard deviation of the variable.

Answer

The imputation will create outliers.

Question 15

A data analyst wants to use a Z-score to standardize a dataset. The variable has a mean of 50 and a standard deviation of 10. What is the Z-score for a raw value of 70?

Accepted Answer

2. The Z-score formula is Z = (X - μ) / σ, where X is the raw value, μ is the mean, and σ is the standard deviation. Plugging in the given values: Z = (70 - 50) / 10 = 20 / 10 = 2. Thus, the raw value of 70 is 2 standard deviations above the mean, corresponding to a Z-score of 2.

Answer

0.5

Answer

20

Answer

-2